Humans rely on spatial information to distinguish sounds and to focus on desirable sound context. Binaural listening may improve intelligibility. Thus, stereo audio may provide better voice intelligibility and speaker separation when multiple people are speaking simultaneously in the same space. This superior intelligibility and separation may enhance a user's experience during a conference call involving multiple speakers on the other end of the call. Stereo audio quality may depend on stereo acoustic echo cancellation (AEC). AEC removes the echo captured by a microphone when a sound is simultaneously played through speakers located where the microphone can hear the sound. Conventionally, stereo AEC may have depended on multi-channel adaptive filtering. However, multi-channel adaptive filtering may have variable and even undesirable performance depending on the level of correlation between stereo signals. For example, when two microphones at a far end are too close together, the signal may be highly correlated, or even identical. When the signal is too highly correlated, conventional multi-channel adaptive filtering may converge at an unacceptably slow rate, if the adaptive filtering even converges at all. This may be referred to as the non-uniqueness issue.
Conventional systems have typically addressed the non-uniqueness issue using stereo de-correlation. Stereo de-correlation may include artificially perturbing or distorting originally input signals to reduce the correlation between channels. However, distorting the input signals may affect voice quality. Additionally, distorting the input signals may alter the stereo spatial image. Further complicating the stereo de-correlation approach to the non-uniqueness issue, if a loopback signal is employed, the loopback signal may contain mono signals rendered by other applications. The presence of these mono signals may frustrate stereo de-correlation.